TY - JOUR

T1 - A Sight on Single-Cell Transcriptomics in Plants Through the Prism of Cell-Based Computational Modeling Approaches

T2 - Benefits and Challenges for Data Analysis

AU - Bobrovskikh, Aleksandr

AU - Doroshkov, Alexey

AU - Mazzoleni, Stefano

AU - Cartenì, Fabrizio

AU - Giannino, Francesco

AU - Zubairova, Ulyana

N1 - Funding Information: Funding. The manuscript concept and analytical review of literature were supported by the Russian Foundation for Basic Research (Project No. 20-04-01112). A NoSelf-UNINA grant project financially supported AB elaborating the general framework for modeling plant morphogenesis. The access to the database of single-cell datasets and its overall analysis was performed using resources of Shared Computational Facilities Center Bioinformatics supported by the State Budget Program (Project No. 0259-2021-0009). Publisher Copyright: © Copyright © 2021 Bobrovskikh, Doroshkov, Mazzoleni, Cartenì, Giannino and Zubairova. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/5/21

Y1 - 2021/5/21

N2 - Single-cell technology is a relatively new and promising way to obtain high-resolution transcriptomic data mostly used for animals during the last decade. However, several scientific groups developed and applied the protocols for some plant tissues. Together with deeply-developed cell-resolution imaging techniques, this achievement opens up new horizons for studying the complex mechanisms of plant tissue architecture formation. While the opportunities for integrating data from transcriptomic to morphogenetic levels in a unified system still present several difficulties, plant tissues have some additional peculiarities. One of the plants’ features is that cell-to-cell communication topology through plasmodesmata forms during tissue growth and morphogenesis and results in mutual regulation of expression between neighboring cells affecting internal processes and cell domain development. Undoubtedly, we must take this fact into account when analyzing single-cell transcriptomic data. Cell-based computational modeling approaches successfully used in plant morphogenesis studies promise to be an efficient way to summarize such novel multiscale data. The inverse problem’s solutions for these models computed on the real tissue templates can shed light on the restoration of individual cells’ spatial localization in the initial plant organ—one of the most ambiguous and challenging stages in single-cell transcriptomic data analysis. This review summarizes new opportunities for advanced plant morphogenesis models, which become possible thanks to single-cell transcriptome data. Besides, we show the prospects of microscopy and cell-resolution imaging techniques to solve several spatial problems in single-cell transcriptomic data analysis and enhance the hybrid modeling framework opportunities.

AB - Single-cell technology is a relatively new and promising way to obtain high-resolution transcriptomic data mostly used for animals during the last decade. However, several scientific groups developed and applied the protocols for some plant tissues. Together with deeply-developed cell-resolution imaging techniques, this achievement opens up new horizons for studying the complex mechanisms of plant tissue architecture formation. While the opportunities for integrating data from transcriptomic to morphogenetic levels in a unified system still present several difficulties, plant tissues have some additional peculiarities. One of the plants’ features is that cell-to-cell communication topology through plasmodesmata forms during tissue growth and morphogenesis and results in mutual regulation of expression between neighboring cells affecting internal processes and cell domain development. Undoubtedly, we must take this fact into account when analyzing single-cell transcriptomic data. Cell-based computational modeling approaches successfully used in plant morphogenesis studies promise to be an efficient way to summarize such novel multiscale data. The inverse problem’s solutions for these models computed on the real tissue templates can shed light on the restoration of individual cells’ spatial localization in the initial plant organ—one of the most ambiguous and challenging stages in single-cell transcriptomic data analysis. This review summarizes new opportunities for advanced plant morphogenesis models, which become possible thanks to single-cell transcriptome data. Besides, we show the prospects of microscopy and cell-resolution imaging techniques to solve several spatial problems in single-cell transcriptomic data analysis and enhance the hybrid modeling framework opportunities.

KW - bioimaging

KW - cell-based computational models

KW - hybrid modeling approach

KW - modeling software

KW - plant morphogenesis

KW - single-cell transcriptomics

KW - spatial gene expression maps

KW - systems biology

UR - http://www.scopus.com/inward/record.url?scp=85107301492&partnerID=8YFLogxK

U2 - 10.3389/fgene.2021.652974

DO - 10.3389/fgene.2021.652974

M3 - Review article

C2 - 34093652

AN - SCOPUS:85107301492

VL - 12

SP - 652974

JO - Frontiers in Genetics

JF - Frontiers in Genetics

SN - 1664-8021

M1 - 652974

ER -